Magdalena Stobińska

Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such a method retains the universality of standard ones, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviors, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We test our model on several tasks, widely recognized to benchmark the performance of (quantum) neuromorphic models. We highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks.

We address the question of the implementation of long-distance device-independent quantum key distribution (DI QKD) by proposing two experimentally viable schemes. Those schemes use only spontaneous parametric down-conversion sources and linear optics. They achieve favorable key rate scaling proportional to the square root of channel transmittance η t , matching the twin-field protocol advantage. We demonstrate positive asymptotic key rates at detector efficiencies as low as 80%, bringing DI QKD within the reach of current superconducting detector technology. Our security analysis employs the entropy accumulation theorem to establish rigorous finite-size bounds, achieving finite-key rates at a detector efficiency of 90%. This work represents a critical milestone toward device-independent security in quantum communication networks, providing experimentalists with practical implementation pathways while maintaining the strongest possible security guarantees against quantum adversaries.

Accurate phase estimation in the presence of unknown phase diffusive noise is a crucial yet challenging task in noisy quantum metrology. This problem is particularly interesting due to the detrimental impact of the associated noise. Here, we investigate the joint estimation of phase and phase diffusion using generalized Holland–Burnett states, known for their experimental accessibility. These states provide performance close to the optimal state in single-parameter phase estimation, even in the presence of photon losses. We adopt a twofold approach by analyzing the joint information extraction through the double homodyne measurement and the joint information availability across all probe states. Through our analysis, we find that the highest sensitivities are obtained by using states created by directing all input photons into one port of a balanced beam splitter. Furthermore, we infer that good levels of sensitivity persist even in the presence of moderate photon losses, illustrating the remarkable resilience of our probe states under lossy conditions.

In recent years, the study of topologically non-trivial structures in one-dimensional models has been dominated by the Su--Schrieffer--Heeger model due to its simplicity in design and its ability to support edge states with robustness to disorder in couplings, protected by chiral and inversion symmetry. Here, we present a novel study on a zigzag quasi-one-dimensional model, which supports topologically protected edge states without relying on conventional symmetries. Our model utilises next-neighbour couplings to mediate edge states and is simultaneously resilient to dissipation, couplings and on-site energy disorders. In order to understand the topological properties of this model, we introduce a novel way to demonstrate the bulk-boundary correspondence of the edge states and construct a topological invariant that returns quantized values. Our study sheds light on the possibility of constructing topological phases in new ways, even in the absence of conventional symmetries, and opens up new avenues for research in this field. In addition, we demonstrate a possible photonic realization of these models with the help of an orbital-induced synthetic flux.

Optical hybrid entanglement can be created between two qubits, one encoded in a single photon and another one in coherent states with opposite phases. It opens the path to a variety of quantum technologies, such as heterogeneous quantum networks, merging continuous- and discrete-variable encoding, and enabling the transport and interconversion of information. However, reliable characterization of the non-local nature of this quantum state is limited so far to full quantum state tomography. Here, we perform a thorough study of Clauser–Horne–Shimony–Holt Bell inequality tests, enabling practical verification of quantum nonlocality for optical hybrid entanglement. We show that a practical violation of this inequality is possible with simple photon number on/off measurements if detection efficiencies stay above 82%. Another approach, based on photon-number parity measurements, requires 94% efficiency but works well in the limit of higher photon populations. Both tests use no postselection of the measurement outcomes and they are free of the fair-sampling hypothesis. Our proposal paves the way to performing loophole-free tests using feasible experimental tasks such as coherent state interference and photon counting.

Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness recurrent neural networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it and to use a quantum-enhanced RNN to overcome these obstacles. The design of the continuous-variable quantum RNN (CV-QRNN) is rooted in the continuous-variable quantum computing paradigm. By performing extensive numerical simulations, we demonstrate that the quantum network is capable of learning-time dependence of several types of temporal data and that it converges to the optimal weights in fewer epochs than a classical network. Furthermore, for a small number of trainable parameters, it can achieve lower losses than its classical counterpart. CV-QRNN can be implemented using commercially available quantum-photonic hardware.